In self-driving technology, an environment-sensing technique of a self-driving vehicle is very important. At present, the sensing of the driving environment of the self-driving vehicle mainly depends on a manner of using a laser radar as a sensor to perform obstacle detection. However, the manner of using a laser radar as a sensor to perform obstacle detection has certain limitations: there are fewer points shed by the laser radar on some small obstacles (such as pedestrians or bicycle riders), so the point cloud data of the obstacle obtained by the laser radar are sparse, and it is very difficult to judge specific obstacle information upon recognition through a recognition algorithm. Loss or inaccuracy of the obstacle information affects the driving policy of the self-driving vehicle and thereby affects the driving safety of the self-driving vehicle. Therefore, it is desirable to provide a method capable of accurately obtaining the specific obstacle information.